Companies across the world spend Billions of Dollars each year on software licenses. According to research analysts at least 20% of software owned by companies is never used. Furthermore companies tend to buy expensive software licenses which they don't fully utilize instead of buying cheaper licenses or use the right software package they need. For instance, purchasing a full Editor license, instead of a Viewer one.
When it comes to moving business applications to the cloud, organizations find themselves blind and overwhelmed by the magnitude of the change hence in most of the cases they will choose to remain with their current expensive business applications instead of transferring the organization to cheaper license models on the cloud (e.g. MS Office vs. Google Apps or Office 365). One of the arguments for not making the shift is that the cloud business applications don't offer the same functionality as the desktop ones. In the case of MS Office the solution is to move light users to the cloud and leave the heavy ones on premise. But again, organizations don't have sufficient tools that can analyze and classify users based on their usage type and working patterns.
The same goes for organizations that are considering VDI (Virtual Desktop Infrastructure) which offers a way to manage software licenses centrally on dedicated servers. The challenge in VDI projects is to divide the organization's user population into different VDI groups that share the same usage profiles, meaning they use the same software applications.
Analyzing the usage of software applications can be challenging in modern computer environments. Knowing whether an application is installed and running is not enough. For instance, there are some applications that will automatically launch during the start of the computer (Auto Run) and will run in the background while the user isn't utilizing them at all. Another example would be users who have full editor licenses while they never edit documents but only read them. As a result, conventional technologies for software metering may be inaccurate or inefficient when it comes to analyzing such cases. It is with respect to these and other considerations that the disclosure made herein is presented.
Taking the above into account, there clearly remains a need, in the field of software licensing and software usage analysis, for better and more efficient systems and methods for accurate software usage metering and classification that are at least partially based on the monitoring of actual software application usage made by users and/or the monitoring of software application related components' operation characteristics.